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The interdisciplinary structure of research on intercultural relations: a co-citation network analysis study

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  • Ruobing Chi

    (University of Hawaii at Manoa
    Shanghai International Studies University)

  • Jonathan Young

    (University of Hawaii at Manoa)

Abstract

This study aims to map the content and structure of the knowledge base of research on intercultural relations as revealed in co-citation networks of 30 years of scholarly publications. Source records for extracting co-citation information are retrieved from Web of Science (1980–2010) through comprehensive keyword search and filtered by manual semantic coding. Exploratory network and content analysis is conducted (1) to discover the development of major research themes and the relations between them over time; (2) to locate representative core publications (the stars) that are highly co-cited with others and those (the bridges) connecting more between rather than within subfields or disciplines. Structural analysis of the co-citation networks identifies a core cluster that contains foundational knowledge of this domain. It is well connected to almost all the other clusters and covers a wide range of subject categories. The evolutionary path of research themes shows trends moving towards (e.g. psychology and business and economics) and away from (e.g. language education and communication) the core cluster over time. Based on the results, a structural framework of the knowledge domain of intercultural relations research is proposed to represent thematic relatedness between topical groups and their relations.

Suggested Citation

  • Ruobing Chi & Jonathan Young, 2013. "The interdisciplinary structure of research on intercultural relations: a co-citation network analysis study," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(1), pages 147-171, July.
    • Handle: RePEc:spr:scient:v:96:y:2013:i:1:d:10.1007_s11192-012-0894-3
    DOI: 10.1007/s11192-012-0894-3
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    References listed on IDEAS

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    1. Loet Leydesdorff & Liwen Vaughan, 2006. "Co‐occurrence matrices and their applications in information science: Extending ACA to the Web environment," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(12), pages 1616-1628, October.
    2. Félix Moya-Anegón & Benjamín Vargas-Quesada & Victor Herrero-Solana & Zaida Chinchilla-Rodríguez & Elena Corera-Álvarez & Francisco J. Munoz-Fernández, 2004. "A new technique for building maps of large scientific domains based on the cocitation of classes and categories," Scientometrics, Springer;Akadémiai Kiadó, vol. 61(1), pages 129-145, September.
    3. Waltman, Ludo & van Eck, Nees Jan & Noyons, Ed C.M., 2010. "A unified approach to mapping and clustering of bibliometric networks," Journal of Informetrics, Elsevier, vol. 4(4), pages 629-635.
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    Citations

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    Cited by:

    1. Carlos Olmeda-Gómez & Maria-Antonia Ovalle-Perandones & Antonio Perianes-Rodríguez, 2017. "Co-word analysis and thematic landscapes in Spanish information science literature, 1985–2014," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(1), pages 195-217, October.
    2. Jingwei Zheng & Ke Zhang & Boya Han & Jiayi Hou, 2023. "Research Interdisciplinarity and Citation Impact: A Network Analysis of Social Networking Sites Research," SAGE Open, , vol. 13(3), pages 21582440231, August.
    3. Moshe Blidstein & Maayan Zhitomirsky-Geffet, 2022. "Towards a new generic framework for citation network generation and analysis in the humanities," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(7), pages 4275-4297, July.
    4. Lu Huang & Yijie Cai & Erdong Zhao & Shengting Zhang & Yue Shu & Jiao Fan, 2022. "Measuring the interdisciplinarity of Information and Library Science interactions using citation analysis and semantic analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6733-6761, November.
    5. Mora, Luca & Deakin, Mark & Reid, Alasdair, 2019. "Combining co-citation clustering and text-based analysis to reveal the main development paths of smart cities," Technological Forecasting and Social Change, Elsevier, vol. 142(C), pages 56-69.
    6. Jiming Hu & Yin Zhang, 2017. "Discovering the interdisciplinary nature of Big Data research through social network analysis and visualization," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(1), pages 91-109, July.
    7. Jingjing Zhang & Yan Yan & Jiancheng Guan, 2015. "Scientific relatedness in solar energy: a comparative study between the USA and China," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(2), pages 1595-1613, February.
    8. Zhichao Ba & Yujie Cao & Jin Mao & Gang Li, 2019. "A hierarchical approach to analyzing knowledge integration between two fields—a case study on medical informatics and computer science," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(3), pages 1455-1486, June.
    9. Zhao, Yi & Liu, Lifan & Zhang, Chengzhi, 2022. "Is coronavirus-related research becoming more interdisciplinary? A perspective of co-occurrence analysis and diversity measure of scientific articles," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    10. Fan, Yangliu & Lehmann, Sune & Blok, Anders, 2022. "Extracting the interdisciplinary specialty structures in social media data-based research: A clustering-based network approach," Journal of Informetrics, Elsevier, vol. 16(3).

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